Formation and properties of the PEO coatings on aluminum-silicon alloys.
Bibliographic record
Abstract
Plasma electrolytic oxidizing (PEO) of aluminium alloys is an advanced technique to deposit a thick and hard ceramic coating on a number of low Si content aluminium alloys. The rapid growth in the tribological applications of high Si cast Al-Si alloys has been motivating this study, i.e., development of a PEO coating with high wear resistance and low friction for high Si content cast Al-Si alloys. In this research, the effect of substrate materials (i.e., silicon contents, Chapter 4) and process parameters (Chapter 5) on the PEO coating formation, microstructure, and composition were investigated in details. An oxide/graphite composite coating with low friction and high wear resistance was particularly developed and studied (Chapter 6). Based on the observations of coating surface morphology change during the treatment, a coating growth model on the Si region was developed in Chapter 4. The PEO process had four stages where each stage was corresponding to different coating surface morphology, composition, and phase structure, characterized by different coating growth mechanisms. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .W364. Source: Masters Abstracts International, Volume: 44-03, page: 1481. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".